Search Results for author: Roger Granada

Found 11 papers, 3 papers with code

Imitating Unknown Policies via Exploration

2 code implementations13 Aug 2020 Nathan Gavenski, Juarez Monteiro, Roger Granada, Felipe Meneguzzi, Rodrigo C. Barros

Behavioral cloning is an imitation learning technique that teaches an agent how to behave through expert demonstrations.

Behavioural cloning

Augmented Behavioral Cloning from Observation

2 code implementations28 Apr 2020 Juarez Monteiro, Nathan Gavenski, Roger Granada, Felipe Meneguzzi, Rodrigo Barros

Imitation from observation is a computational technique that teaches an agent on how to mimic the behavior of an expert by observing only the sequence of states from the expert demonstrations.

Behavioural cloning

HAPRec: Hybrid Activity and Plan Recognizer

no code implementations28 Apr 2020 Roger Granada, Ramon Fraga Pereira, Juarez Monteiro, Leonardo Amado, Rodrigo C. Barros, Duncan Ruiz, Felipe Meneguzzi

Computer-based assistants have recently attracted much interest due to its applicability to ambient assisted living.

Action Recognition

Classifying Norm Conflicts using Learned Semantic Representations

no code implementations13 May 2019 João Paulo Aires, Roger Granada, Juarez Monteiro, Rodrigo C. Barros, Felipe Meneguzzi

While most social norms are informal, they are often formalized by companies in contracts to regulate trades of goods and services.

Evaluating the Complementarity of Taxonomic Relation Extraction Methods Across Different Languages

no code implementations8 Nov 2018 Roger Granada, Renata Vieira, Cassia Trojahn, Nathalie Aussenac-Gilles

The automatic construction of concept hierarchies from texts is a complex task and much work have been proposing approaches to better extract relations between concepts.

Relation Relation Extraction

LSTM-Based Goal Recognition in Latent Space

no code implementations15 Aug 2018 Leonardo Amado, João Paulo Aires, Ramon Fraga Pereira, Maurício C. Magnaguagno, Roger Granada, Felipe Meneguzzi

Approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms capable of recognizing goals.

General Classification

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